We complete the discussion related to the implementation details, that we had in Sec. 5.2 here.
We ran our experiments on a server with 8 Nvidia V100 GPUs with an effective batch size of 8. We use SGD with a learning rate of \(0.01\) . Each task is learned for 8 epochs (\(\sim 50\) k iterations). The queue size of the feature store is set to 20. We initiate clustering after 1k iterations and update the cluster prototypes after each 3k iterations with a momentum parameter of \(0.99\) . Euclidean distance is used as the distance function \(\mathcal {D}\)  in Eqn. 1. The margin (\(\Delta \) ) is set as 10. For auto-labelling the unknowns in the RPN, we pick the top-1 background proposal, sorted by its objectness score. The temperature parameter in the energy based classification head is set to 1. The code is implemented in PyTorch [1]} using Detectron 2 [2]}. Reliability library [3]} was used for modelling the energy distributions.
We release all our codes publicly for foster reproducible research: https://github.com/JosephKJ/OWOD.
